How to better manage your data science team's workflow

Presented by

Yochay Ettun

About this talk

Scaling AI starts with proper management of data science teams. A common problem data science managers face is how to structure teams for efficiency and communicating results to business leaders. The hard part is streamlining the data science process to eliminate wait time, and easily transition between science and engineering, and business goals. While a standard agile has never been created for data science teams there are many methods that can make machine learning development easier. This workshop will give you the proper tools and tactics to manage the entire lifecycle of your machine learning projects, from research to exploration to development and production. Yochay will go over the different roles and responsibilities of a data science team and how to better collaborate on machine learning projects. You’ll learn to manage models, bridge between science and engineering, and save time with reproducible results. In addition, you’ll leave with the tools to more effectively communicate results to your business unit. What you’ll learn: - How to build a data science workflow for reproducibility - Model management and experiment tracking - Tools for easy collaboration - Tools to communicate results to business unit - How to transition between science and engineering

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Learn from various data science and engineering experts about key topics for successful machine learning. cnvrg.io will provide you with hands-on tutorials and workshops about the top methods in data science team management, and MLOps getting you from research to production. Stay ahead with the latest developments in auto-adaptive machine learning and CI/CD for machine learning. Learn the latest methods for machine learning model management and deployment with open source tools. Find answers on how to enhance team collaboration in your data science department, and smoothly bridge science to engineering.